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Fourth Canadian Conference on Computer and Robot Vision (CRV '07)
A Framework for 3D Hand Tracking and Gesture Recognition using Elements of Genetic Programming
Montreal, Quebec, Canada
May 28-May 30
ISBN: 0-7695-2786-8
ASCII Text
x 
 
Ayman El-Sawah, Chris Joslin, Nicolas D. Georganas, Emil M. Petriu, "A Framework for 3D Hand Tracking and Gesture Recognition using Elements of Genetic Programming," Computer and Robot Vision, Canadian Conference, pp. 495-502, Fourth Canadian Conference on Computer and Robot Vision (CRV '07), 2007.
 
 
BibTex
x
 
@article{ 10.1109/CRV.2007.3,
author = {Ayman El-Sawah and Chris Joslin and Nicolas D. Georganas and Emil M. Petriu},
title = {A Framework for 3D Hand Tracking and Gesture Recognition using Elements of Genetic Programming},
journal ={Computer and Robot Vision, Canadian Conference},
volume = {0},
year = {2007},
isbn = {0-7695-2786-8},
pages = {495-502},
doi = {http://doi.ieeecomputersociety.org/10.1109/CRV.2007.3},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Computer and Robot Vision, Canadian Conference
TI - A Framework for 3D Hand Tracking and Gesture Recognition using Elements of Genetic Programming
SN - 0-7695-2786-8
SP495
EP502
A1 - Ayman El-Sawah,
A1 - Chris Joslin,
A1 - Nicolas D. Georganas,
A1 - Emil M. Petriu,
PY - 2007
KW - null
VL - 0
JA - Computer and Robot Vision, Canadian Conference
ER -
 
Ayman El-Sawah, University of Ottawa
Chris Joslin, University of Ottawa
Nicolas D. Georganas, University of Ottawa
Emil M. Petriu, University of Ottawa
In this paper we present a framework for 3D hand tracking and dynamic gesture recognition using a single camera. Hand tracking is performed in a two step process: we first generate 3D hand posture hypothesis using geometric and kinematics inverse transformations, and then validate the hypothesis by projecting the postures on the image plane and comparing the projected model with the ground truth using a probabilistic observation model. Dynamic gesture recognition is performed using a Dynamic Bayesian Network model. The framework utilizes elements of soft computing to resolve the ambiguity inherent in vision-based tracking by producing a fuzzy hand posture output by the hand tracking module and feeding back potential posture hypothesis from the gesture recognition module.
Citation:
Ayman El-Sawah, Chris Joslin, Nicolas D. Georganas, Emil M. Petriu, "A Framework for 3D Hand Tracking and Gesture Recognition using Elements of Genetic Programming," crv, pp.495-502, Fourth Canadian Conference on Computer and Robot Vision (CRV '07), 2007
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